Patient Reported Outcome Measures (PROMs) have arrived in sports and exercise medicine: Why do they matter?
Bibliographic record
Abstract
Clinicians and administrators have a professional obligation to contribute (OTC) to improvement of healthcare quality. At the same time, participation in embedded research poses risks to healthcare institutions. Disclosure of an institution’s sensitive information could endanger relationships with patients and undermine its reputation. The existing ethical framework (EF) for learning healthcare systems (LHSs) does not address the conflict between the OTC and institutional interests. Ethical guidance and policy regulation are needed to create a safe environment for embedded research. In this article we analyse the EF for LHSs and the concept of professionalism. We suggest that the EF should be supplemented with an obligation to protect provider’s legitimate interests. We define legitimate interests as those that enable providers to discharge their primary duties. We argue that both the OTC and the obligation to protect legitimate interests are grounded in the concept of medical professionalism and can be understood as a matter of contract between a democratic society and medical professionals. The proposed supplemented EF can be implemented into a regulatory system in three different ways: the self-regulating: where providers decide themselves how to balance the ethical claims, the centralised: where a governmental institution decides the right balance between providers’ interests and interests of a health system; and the mediating: where medical professionals, the state and patients negotiate their interests. Our article contributes to the discussion on ethical relevance of providers’ interests and the regulatory model for weighing opposite interests in LHSs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.156 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".